{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题三：\n",
    "\n",
    "建立辛烷值（RON）损失预测模型：采用上述样本和建模主要变量，通过数据挖掘技术建立辛烷值（RON）损失预测模型，并进行模型验证。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from hmmlearn.hmm import GaussianHMM\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import Lasso  # 线性回归\n",
    "from sklearn.model_selection import train_test_split  # 这里是引用了交叉验证\n",
    "\n",
    "# Suppress warnings from pandas\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams['font.family'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r samples_data\n",
    "# cluster 1st\n",
    "%store -r model_features_1st\n",
    "%store -r produc_features_1st\n",
    "%store -r weight_1st\n",
    "# cluster 2nd\n",
    "%store -r model_features_2nd\n",
    "%store -r produc_features_2nd\n",
    "%store -r weight_2nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>S-ZORB.FC_1201.PV</th>\n",
       "      <th>S-ZORB.FC_5103.DACA</th>\n",
       "      <th>S-ZORB.FC_2702.DACA</th>\n",
       "      <th>S-ZORB.TE_1107.DACA</th>\n",
       "      <th>S-ZORB.BS_AT_2402.PV</th>\n",
       "      <th>S-ZORB.LT_2901.DACA</th>\n",
       "      <th>S-ZORB.LC_5102.DACA</th>\n",
       "      <th>S-ZORB.FT_1301.DACA</th>\n",
       "      <th>S-ZORB.LC_1201.PV</th>\n",
       "      <th>S-ZORB.AC_6001.PV</th>\n",
       "      <th>...</th>\n",
       "      <th>S-ZORB.TE_5202.PV</th>\n",
       "      <th>S-ZORB.PDT_3502.DACA</th>\n",
       "      <th>S-ZORB.TE_1501.DACA</th>\n",
       "      <th>S-ZORB.TE_1001.PV</th>\n",
       "      <th>S-ZORB.HIC_2533.AUTOMANA.OP</th>\n",
       "      <th>S-ZORB.FT_1001.PV</th>\n",
       "      <th>S-ZORB.PDT_3002.DACA</th>\n",
       "      <th>S-ZORB.LT_3801.DACA</th>\n",
       "      <th>原料性质</th>\n",
       "      <th>时间</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>D104去稳定塔流量</th>\n",
       "      <th>稳定塔顶回流流量</th>\n",
       "      <th>D-110底流化N2流量</th>\n",
       "      <th>E-101D壳程出口管温度</th>\n",
       "      <th>闭锁料斗氧含量</th>\n",
       "      <th>D-109吸附剂料位</th>\n",
       "      <th>D-201水包界位</th>\n",
       "      <th>K-103出口去K-101出口管流量</th>\n",
       "      <th>D104液面</th>\n",
       "      <th>加热炉氧含量</th>\n",
       "      <th>...</th>\n",
       "      <th>精制汽油出装置温度</th>\n",
       "      <th>ME-109过滤器差压</th>\n",
       "      <th>D-122入口管温度</th>\n",
       "      <th>原料进装置温度</th>\n",
       "      <th>HV2533手操器</th>\n",
       "      <th>催化汽油进装置总流量</th>\n",
       "      <th>ME-105过滤器压差</th>\n",
       "      <th>D-125液位</th>\n",
       "      <th>辛烷值RON</th>\n",
       "      <th>Unnamed: 1_level_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>116.260115</td>\n",
       "      <td>10935.59125</td>\n",
       "      <td>31.435405</td>\n",
       "      <td>124.205312</td>\n",
       "      <td>0.066352</td>\n",
       "      <td>37.612641</td>\n",
       "      <td>49.308646</td>\n",
       "      <td>2659.89535</td>\n",
       "      <td>50.034019</td>\n",
       "      <td>2.202533</td>\n",
       "      <td>...</td>\n",
       "      <td>37.795462</td>\n",
       "      <td>10.687697</td>\n",
       "      <td>21.632472</td>\n",
       "      <td>69.928027</td>\n",
       "      <td>100.0</td>\n",
       "      <td>117.720835</td>\n",
       "      <td>4.361624</td>\n",
       "      <td>-0.385746</td>\n",
       "      <td>0.196377</td>\n",
       "      <td>2017-04-17 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>115.351760</td>\n",
       "      <td>11831.05375</td>\n",
       "      <td>31.629264</td>\n",
       "      <td>123.123952</td>\n",
       "      <td>0.062292</td>\n",
       "      <td>44.545153</td>\n",
       "      <td>45.484899</td>\n",
       "      <td>3008.69925</td>\n",
       "      <td>50.013484</td>\n",
       "      <td>2.191190</td>\n",
       "      <td>...</td>\n",
       "      <td>35.154480</td>\n",
       "      <td>10.585208</td>\n",
       "      <td>21.812414</td>\n",
       "      <td>70.397828</td>\n",
       "      <td>100.0</td>\n",
       "      <td>125.895110</td>\n",
       "      <td>4.313126</td>\n",
       "      <td>-0.094022</td>\n",
       "      <td>-0.370934</td>\n",
       "      <td>2017-04-19 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>115.647893</td>\n",
       "      <td>11475.92050</td>\n",
       "      <td>31.730374</td>\n",
       "      <td>122.736107</td>\n",
       "      <td>0.057254</td>\n",
       "      <td>55.308304</td>\n",
       "      <td>40.356015</td>\n",
       "      <td>3324.24260</td>\n",
       "      <td>50.051713</td>\n",
       "      <td>2.196760</td>\n",
       "      <td>...</td>\n",
       "      <td>36.037239</td>\n",
       "      <td>11.383706</td>\n",
       "      <td>21.802443</td>\n",
       "      <td>69.643416</td>\n",
       "      <td>100.0</td>\n",
       "      <td>123.501397</td>\n",
       "      <td>4.388240</td>\n",
       "      <td>-0.014480</td>\n",
       "      <td>-0.370934</td>\n",
       "      <td>2017-04-21 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>115.434765</td>\n",
       "      <td>13514.39125</td>\n",
       "      <td>29.777903</td>\n",
       "      <td>123.030560</td>\n",
       "      <td>0.060190</td>\n",
       "      <td>66.512932</td>\n",
       "      <td>49.152954</td>\n",
       "      <td>2911.15755</td>\n",
       "      <td>50.011796</td>\n",
       "      <td>2.182150</td>\n",
       "      <td>...</td>\n",
       "      <td>34.735130</td>\n",
       "      <td>10.937725</td>\n",
       "      <td>21.932511</td>\n",
       "      <td>70.428077</td>\n",
       "      <td>100.0</td>\n",
       "      <td>127.956985</td>\n",
       "      <td>0.646210</td>\n",
       "      <td>0.077742</td>\n",
       "      <td>-0.597858</td>\n",
       "      <td>2017-04-24 08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>114.662462</td>\n",
       "      <td>11675.13325</td>\n",
       "      <td>28.955438</td>\n",
       "      <td>124.334542</td>\n",
       "      <td>0.052389</td>\n",
       "      <td>7.021087</td>\n",
       "      <td>69.701451</td>\n",
       "      <td>2933.87270</td>\n",
       "      <td>50.039356</td>\n",
       "      <td>2.196662</td>\n",
       "      <td>...</td>\n",
       "      <td>34.331862</td>\n",
       "      <td>10.128979</td>\n",
       "      <td>22.422047</td>\n",
       "      <td>72.644269</td>\n",
       "      <td>100.0</td>\n",
       "      <td>124.803388</td>\n",
       "      <td>4.531366</td>\n",
       "      <td>0.025413</td>\n",
       "      <td>-0.597858</td>\n",
       "      <td>2017-04-26 08:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    S-ZORB.FC_1201.PV S-ZORB.FC_5103.DACA S-ZORB.FC_2702.DACA  \\\n",
       "           D104去稳定塔流量            稳定塔顶回流流量        D-110底流化N2流量   \n",
       "324        116.260115         10935.59125           31.435405   \n",
       "323        115.351760         11831.05375           31.629264   \n",
       "322        115.647893         11475.92050           31.730374   \n",
       "321        115.434765         13514.39125           29.777903   \n",
       "320        114.662462         11675.13325           28.955438   \n",
       "\n",
       "    S-ZORB.TE_1107.DACA S-ZORB.BS_AT_2402.PV S-ZORB.LT_2901.DACA  \\\n",
       "          E-101D壳程出口管温度              闭锁料斗氧含量          D-109吸附剂料位   \n",
       "324          124.205312             0.066352           37.612641   \n",
       "323          123.123952             0.062292           44.545153   \n",
       "322          122.736107             0.057254           55.308304   \n",
       "321          123.030560             0.060190           66.512932   \n",
       "320          124.334542             0.052389            7.021087   \n",
       "\n",
       "    S-ZORB.LC_5102.DACA S-ZORB.FT_1301.DACA S-ZORB.LC_1201.PV  \\\n",
       "              D-201水包界位  K-103出口去K-101出口管流量            D104液面   \n",
       "324           49.308646          2659.89535         50.034019   \n",
       "323           45.484899          3008.69925         50.013484   \n",
       "322           40.356015          3324.24260         50.051713   \n",
       "321           49.152954          2911.15755         50.011796   \n",
       "320           69.701451          2933.87270         50.039356   \n",
       "\n",
       "    S-ZORB.AC_6001.PV  ... S-ZORB.TE_5202.PV S-ZORB.PDT_3502.DACA  \\\n",
       "               加热炉氧含量  ...         精制汽油出装置温度          ME-109过滤器差压   \n",
       "324          2.202533  ...         37.795462            10.687697   \n",
       "323          2.191190  ...         35.154480            10.585208   \n",
       "322          2.196760  ...         36.037239            11.383706   \n",
       "321          2.182150  ...         34.735130            10.937725   \n",
       "320          2.196662  ...         34.331862            10.128979   \n",
       "\n",
       "    S-ZORB.TE_1501.DACA S-ZORB.TE_1001.PV S-ZORB.HIC_2533.AUTOMANA.OP  \\\n",
       "             D-122入口管温度           原料进装置温度                   HV2533手操器   \n",
       "324           21.632472         69.928027                       100.0   \n",
       "323           21.812414         70.397828                       100.0   \n",
       "322           21.802443         69.643416                       100.0   \n",
       "321           21.932511         70.428077                       100.0   \n",
       "320           22.422047         72.644269                       100.0   \n",
       "\n",
       "    S-ZORB.FT_1001.PV S-ZORB.PDT_3002.DACA S-ZORB.LT_3801.DACA      原料性质  \\\n",
       "           催化汽油进装置总流量          ME-105过滤器压差             D-125液位    辛烷值RON   \n",
       "324        117.720835             4.361624           -0.385746  0.196377   \n",
       "323        125.895110             4.313126           -0.094022 -0.370934   \n",
       "322        123.501397             4.388240           -0.014480 -0.370934   \n",
       "321        127.956985             0.646210            0.077742 -0.597858   \n",
       "320        124.803388             4.531366            0.025413 -0.597858   \n",
       "\n",
       "                     时间  \n",
       "     Unnamed: 1_level_1  \n",
       "324 2017-04-17 08:00:00  \n",
       "323 2017-04-19 08:00:00  \n",
       "322 2017-04-21 08:00:00  \n",
       "321 2017-04-24 08:00:00  \n",
       "320 2017-04-26 08:00:00  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_features_1st.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_features_diff = np.diff(model_features_1st.iloc[1:, :-1])\n",
    "produc_features_diff = np.diff(produc_features_1st.iloc[1:, -1:])\n",
    "\n",
    "X = np.column_stack([model_features_diff, produc_features_diff])\n",
    "\n",
    "model = GaussianHMM(n_components=5, covariance_type=\"full\", n_iter=20000).fit(X)\n",
    "hidden_states = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2304x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Date = model_features_1st.iloc[1:, -1:].values\n",
    "Date = Date.reshape(Date.shape[0], )\n",
    "Obser = produc_features_1st.iloc[1:, -1:].values\n",
    "Obser = Obser.reshape(Obser.shape[0], )\n",
    "plt.figure(figsize=(32, 8))\n",
    "for i in range(model.n_components):\n",
    "    pos = (hidden_states==i)\n",
    "    plt.plot_date(Date[pos], Obser[pos], 'o', label='hidden state %d'%i)\n",
    "    plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2304x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = pd.DataFrame({'Date':Date, 'Obser':Obser, 'state':hidden_states}).set_index('Date')\n",
    "plt.figure(figsize=(32, 8)) \n",
    "for i in range(model.n_components):\n",
    "    pos = (hidden_states==i)\n",
    "    pos = np.append(0, pos[:-1])  # 第二天进行买入操作\n",
    "    df = res.Obser\n",
    "    res['state_ret%s'%i] = df.multiply(pos)\n",
    "    plt.plot_date(Date, np.exp(res['state_ret%s'%i].cumsum()), '-', label='hidden state %d'%i)\n",
    "    plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2304x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'b', 'y', 'g', 'cyan']\n",
    "fig = plt.figure(figsize=(32, 8)) \n",
    "for j in range(len(Obser) - 1):\n",
    "    for i in range(model.n_components):\n",
    "        if hidden_states[j] == i:\n",
    "            plt.plot([Date[j], Date[j+1]], [Obser[j], Obser[j+1]], color = colors[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>S-ZORB.TE_5202.PV</th>\n",
       "      <th>S-ZORB.FT_9102.PV</th>\n",
       "      <th>S-ZORB.LT_9001.DACA</th>\n",
       "      <th>S-ZORB.TC_2201.OP</th>\n",
       "      <th>S-ZORB.LC_1201.PV</th>\n",
       "      <th>S-ZORB.PT_6002.PV</th>\n",
       "      <th>S-ZORB.PDT_2606.DACA</th>\n",
       "      <th>S-ZORB.FT_3501.DACA</th>\n",
       "      <th>S-ZORB.AT-0011.DACA.PV</th>\n",
       "      <th>S-ZORB.TE_7504.DACA</th>\n",
       "      <th>...</th>\n",
       "      <th>S-ZORB.TE_9002.DACA</th>\n",
       "      <th>S-ZORB.LI_9102.DACA</th>\n",
       "      <th>S-ZORB.PDC_2607.PV</th>\n",
       "      <th>S-ZORB.AT-0008.DACA.PV</th>\n",
       "      <th>S-ZORB.TXE_2203A.DACA</th>\n",
       "      <th>S-ZORB.AI_2903.PV</th>\n",
       "      <th>S-ZORB.TE_2608.DACA</th>\n",
       "      <th>S-ZORB.CAL_H2.PV</th>\n",
       "      <th>S-ZORB.FT_9201.PV</th>\n",
       "      <th>原料性质</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>精制汽油出装置温度</th>\n",
       "      <th>火炬气排放流量</th>\n",
       "      <th>D-203底部液位</th>\n",
       "      <th>EH101出口</th>\n",
       "      <th>D104液面</th>\n",
       "      <th>加热炉炉膛压力</th>\n",
       "      <th>R-102底滑阀差压</th>\n",
       "      <th>循环氢至闭锁料斗料腿流量</th>\n",
       "      <th>S_ZORB AT-0011</th>\n",
       "      <th>K-102A排气温度</th>\n",
       "      <th>...</th>\n",
       "      <th>D-203顶部出口管温度</th>\n",
       "      <th>D-204液位</th>\n",
       "      <th>R102转剂线压差</th>\n",
       "      <th>S_ZORB AT-0008</th>\n",
       "      <th>EH-101加热元件温度</th>\n",
       "      <th>再生烟气氧含量</th>\n",
       "      <th>R-102底部锥段温度</th>\n",
       "      <th>氢油比</th>\n",
       "      <th>循环水进装置流量</th>\n",
       "      <th>辛烷值RON</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>37.805150</td>\n",
       "      <td>1.173297e+07</td>\n",
       "      <td>-1.365035</td>\n",
       "      <td>10.0</td>\n",
       "      <td>49.996073</td>\n",
       "      <td>-0.171115</td>\n",
       "      <td>3.390523</td>\n",
       "      <td>-0.018787</td>\n",
       "      <td>0.670982</td>\n",
       "      <td>19.24878</td>\n",
       "      <td>...</td>\n",
       "      <td>40.187005</td>\n",
       "      <td>60.430943</td>\n",
       "      <td>27.202543</td>\n",
       "      <td>0.693989</td>\n",
       "      <td>377.658987</td>\n",
       "      <td>5090.635100</td>\n",
       "      <td>441.632868</td>\n",
       "      <td>0.249154</td>\n",
       "      <td>490.472150</td>\n",
       "      <td>0.650225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>34.682821</td>\n",
       "      <td>8.490662e+06</td>\n",
       "      <td>-1.357267</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50.038723</td>\n",
       "      <td>-0.254149</td>\n",
       "      <td>12.055007</td>\n",
       "      <td>-0.019109</td>\n",
       "      <td>0.680290</td>\n",
       "      <td>19.24878</td>\n",
       "      <td>...</td>\n",
       "      <td>35.814276</td>\n",
       "      <td>54.121831</td>\n",
       "      <td>21.407790</td>\n",
       "      <td>0.700673</td>\n",
       "      <td>368.811958</td>\n",
       "      <td>0.035802</td>\n",
       "      <td>477.157017</td>\n",
       "      <td>0.287452</td>\n",
       "      <td>493.352560</td>\n",
       "      <td>-1.278630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>36.886109</td>\n",
       "      <td>8.082110e+06</td>\n",
       "      <td>-1.366509</td>\n",
       "      <td>10.0</td>\n",
       "      <td>49.984607</td>\n",
       "      <td>-0.262551</td>\n",
       "      <td>12.737633</td>\n",
       "      <td>-0.018902</td>\n",
       "      <td>0.672048</td>\n",
       "      <td>19.24878</td>\n",
       "      <td>...</td>\n",
       "      <td>40.150302</td>\n",
       "      <td>43.995125</td>\n",
       "      <td>19.023768</td>\n",
       "      <td>0.690734</td>\n",
       "      <td>380.936382</td>\n",
       "      <td>0.315823</td>\n",
       "      <td>474.041060</td>\n",
       "      <td>0.281411</td>\n",
       "      <td>498.007182</td>\n",
       "      <td>-0.711320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>37.129522</td>\n",
       "      <td>7.809743e+06</td>\n",
       "      <td>-1.368641</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50.031432</td>\n",
       "      <td>-0.284343</td>\n",
       "      <td>12.114046</td>\n",
       "      <td>-0.020096</td>\n",
       "      <td>0.663611</td>\n",
       "      <td>19.24878</td>\n",
       "      <td>...</td>\n",
       "      <td>39.572968</td>\n",
       "      <td>60.397504</td>\n",
       "      <td>18.130908</td>\n",
       "      <td>0.698439</td>\n",
       "      <td>375.278435</td>\n",
       "      <td>0.262105</td>\n",
       "      <td>467.114460</td>\n",
       "      <td>0.271662</td>\n",
       "      <td>499.805440</td>\n",
       "      <td>-0.824782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>36.803966</td>\n",
       "      <td>6.175536e+06</td>\n",
       "      <td>-1.401113</td>\n",
       "      <td>10.0</td>\n",
       "      <td>49.601571</td>\n",
       "      <td>-0.267372</td>\n",
       "      <td>8.104848</td>\n",
       "      <td>-0.014209</td>\n",
       "      <td>0.655061</td>\n",
       "      <td>19.24878</td>\n",
       "      <td>...</td>\n",
       "      <td>42.269520</td>\n",
       "      <td>61.336831</td>\n",
       "      <td>10.925054</td>\n",
       "      <td>0.709054</td>\n",
       "      <td>373.242802</td>\n",
       "      <td>0.379689</td>\n",
       "      <td>404.505900</td>\n",
       "      <td>0.273399</td>\n",
       "      <td>457.970913</td>\n",
       "      <td>-0.484396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    S-ZORB.TE_5202.PV S-ZORB.FT_9102.PV S-ZORB.LT_9001.DACA S-ZORB.TC_2201.OP  \\\n",
       "            精制汽油出装置温度           火炬气排放流量           D-203底部液位           EH101出口   \n",
       "302         37.805150      1.173297e+07           -1.365035              10.0   \n",
       "291         34.682821      8.490662e+06           -1.357267              10.0   \n",
       "290         36.886109      8.082110e+06           -1.366509              10.0   \n",
       "289         37.129522      7.809743e+06           -1.368641              10.0   \n",
       "284         36.803966      6.175536e+06           -1.401113              10.0   \n",
       "\n",
       "    S-ZORB.LC_1201.PV S-ZORB.PT_6002.PV S-ZORB.PDT_2606.DACA  \\\n",
       "               D104液面           加热炉炉膛压力           R-102底滑阀差压   \n",
       "302         49.996073         -0.171115             3.390523   \n",
       "291         50.038723         -0.254149            12.055007   \n",
       "290         49.984607         -0.262551            12.737633   \n",
       "289         50.031432         -0.284343            12.114046   \n",
       "284         49.601571         -0.267372             8.104848   \n",
       "\n",
       "    S-ZORB.FT_3501.DACA S-ZORB.AT-0011.DACA.PV S-ZORB.TE_7504.DACA  ...  \\\n",
       "           循环氢至闭锁料斗料腿流量         S_ZORB AT-0011          K-102A排气温度  ...   \n",
       "302           -0.018787               0.670982            19.24878  ...   \n",
       "291           -0.019109               0.680290            19.24878  ...   \n",
       "290           -0.018902               0.672048            19.24878  ...   \n",
       "289           -0.020096               0.663611            19.24878  ...   \n",
       "284           -0.014209               0.655061            19.24878  ...   \n",
       "\n",
       "    S-ZORB.TE_9002.DACA S-ZORB.LI_9102.DACA S-ZORB.PDC_2607.PV  \\\n",
       "           D-203顶部出口管温度             D-204液位          R102转剂线压差   \n",
       "302           40.187005           60.430943          27.202543   \n",
       "291           35.814276           54.121831          21.407790   \n",
       "290           40.150302           43.995125          19.023768   \n",
       "289           39.572968           60.397504          18.130908   \n",
       "284           42.269520           61.336831          10.925054   \n",
       "\n",
       "    S-ZORB.AT-0008.DACA.PV S-ZORB.TXE_2203A.DACA S-ZORB.AI_2903.PV  \\\n",
       "            S_ZORB AT-0008          EH-101加热元件温度           再生烟气氧含量   \n",
       "302               0.693989            377.658987       5090.635100   \n",
       "291               0.700673            368.811958          0.035802   \n",
       "290               0.690734            380.936382          0.315823   \n",
       "289               0.698439            375.278435          0.262105   \n",
       "284               0.709054            373.242802          0.379689   \n",
       "\n",
       "    S-ZORB.TE_2608.DACA S-ZORB.CAL_H2.PV S-ZORB.FT_9201.PV      原料性质  \n",
       "            R-102底部锥段温度              氢油比          循环水进装置流量    辛烷值RON  \n",
       "302          441.632868         0.249154        490.472150  0.650225  \n",
       "291          477.157017         0.287452        493.352560 -1.278630  \n",
       "290          474.041060         0.281411        498.007182 -0.711320  \n",
       "289          467.114460         0.271662        499.805440 -0.824782  \n",
       "284          404.505900         0.273399        457.970913 -0.484396  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if ('时间', 'Unnamed: 1_level_1') in model_features_2nd.keys():\n",
    "    del model_features_2nd[('时间', 'Unnamed: 1_level_1')]\n",
    "model_features_2nd.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对于 类别-1 进行线性回归建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape=(133, 33)\n",
      "y_train.shape=(133, 1)\n",
      "X_test.shape=(45, 33)\n",
      "y_test.shape=(45, 1)\n"
     ]
    }
   ],
   "source": [
    "# 构建训练集和测试集 cluster 1st\n",
    "X = model_features_1st\n",
    "y = produc_features_1st[[('产品性质', 'RON损失\\n（不是变量）')]]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print('X_train.shape={}\\ny_train.shape={}\\nX_test.shape={}\\ny_test.shape={}'.format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "invalid type promotion",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-cc9ba8444f43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Applying LinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel_1st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLasso\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel_1st\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0my_train_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_1st\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0my_test_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_1st\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/python(chenhao)/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input)\u001b[0m\n\u001b[1;32m    757\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0mX_copied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_intercept\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m             X, y = self._validate_data(X, y, accept_sparse='csc',\n\u001b[0m\u001b[1;32m    760\u001b[0m                                        \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'F'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m                                        \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/python(chenhao)/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 432\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/python(chenhao)/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     70\u001b[0m                           FutureWarning)\n\u001b[1;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/python(chenhao)/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m    793\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y cannot be None\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[1;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/python(chenhao)/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     70\u001b[0m                           FutureWarning)\n\u001b[1;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/python(chenhao)/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdtypes_orig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m             \u001b[0mdtype_orig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdtypes_orig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdtype_numeric\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mresult_type\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: invalid type promotion"
     ]
    }
   ],
   "source": [
    "# 简单线性回归\n",
    "# Applying LinearRegression\n",
    "model_1st = Lasso(alpha=0.1)\n",
    "model_1st.fit(X_train, y_train)\n",
    "y_train_pred = model_1st.predict(X_train)\n",
    "y_test_pred = model_1st.predict(X_test)\n",
    "y_pred = model_1st.predict(X)\n",
    "print ('MSE train:%.3f, test:%.3f, whole:%.3f' % (metrics.mean_squared_error(y_train, y_train_pred), metrics.mean_squared_error(y_test, y_test_pred), metrics.mean_squared_error(y, y_pred)))\n",
    "print ('R^2 train:%.3f, test:%.3f, whole:%.3f' % (metrics.r2_score(y_train, y_train_pred), metrics.r2_score(y_test, y_test_pred), metrics.r2_score(y, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (32, 24))\n",
    "plt.subplot(311)\n",
    "plt.plot(range(len(y_train)), y_train_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_train)), y_train, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"训练集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(312)\n",
    "plt.plot(range(len(y_test)), y_test_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"测试集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(313)\n",
    "plt.plot(range(len(y)), y_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y)), y, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"样本集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对于 类别-2 进行线性回归建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建训练集和测试集 cluster 2nd\n",
    "X = model_features_2nd\n",
    "y = produc_features_2nd[[('产品性质', 'RON损失\\n（不是变量）')]]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print('X_train.shape={}\\ny_train.shape={}\\nX_test.shape={}\\ny_test.shape={}'.format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 简单线性回归\n",
    "# Applying LinearRegression\n",
    "model_2nd = Lasso(alpha=0.1)\n",
    "model_2nd.fit(X_train, y_train)\n",
    "y_train_pred = model_2nd.predict(X_train)\n",
    "y_test_pred = model_2nd.predict(X_test)\n",
    "y_pred = model_2nd.predict(X)\n",
    "print ('MSE train:%.3f, test:%.3f, whole:%.3f' % (metrics.mean_squared_error(y_train, y_train_pred), metrics.mean_squared_error(y_test, y_test_pred), metrics.mean_squared_error(y, y_pred)))\n",
    "print ('R^2 train:%.3f, test:%.3f, whole:%.3f' % (metrics.r2_score(y_train, y_train_pred), metrics.r2_score(y_test, y_test_pred), metrics.r2_score(y, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (32, 24))\n",
    "plt.subplot(311)\n",
    "plt.plot(range(len(y_train)), y_train_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_train)), y_train, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"训练集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(312)\n",
    "plt.plot(range(len(y_test)), y_test_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"测试集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(313)\n",
    "plt.plot(range(len(y)), y_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y)), y, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"样本集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 进行加权求和预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1st = samples_data[model_features_1st.keys()]\n",
    "data_2nd = samples_data[model_features_2nd.keys()]\n",
    "y = samples_data[[('产品性质', 'RON损失\\n（不是变量）')]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_1st.predict(data_1st) + model_2nd.predict(data_2nd)\n",
    "print ('MSE whole:%.3f' % (metrics.mean_squared_error(y, y_pred)))\n",
    "print ('R^2 whole:%.3f' % (metrics.r2_score(y, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (32, 8))\n",
    "plt.plot(range(len(y)), y_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y)), y, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"样本集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择各聚类的模型的特征的并集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = samples_data[list(set(model_features_1st.keys()).union(set(model_features_2nd.keys())))]\n",
    "y = samples_data[[('产品性质', 'RON损失\\n（不是变量）')]]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print('X_train.shape={}\\ny_train.shape={}\\nX_test.shape={}\\ny_test.shape={}'.format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 简单线性回归\n",
    "# Applying LinearRegression\n",
    "model_3rd = Lasso(alpha=0.1)\n",
    "model_3rd.fit(X_train, y_train)\n",
    "y_train_pred = model_3rd.predict(X_train)\n",
    "y_test_pred = model_3rd.predict(X_test)\n",
    "y_pred = model_3rd.predict(X)\n",
    "print ('MSE train:%.3f, test:%.3f, whole:%.3f' % (metrics.mean_squared_error(y_train, y_train_pred), metrics.mean_squared_error(y_test, y_test_pred), metrics.mean_squared_error(y, y_pred)))\n",
    "print ('R^2 train:%.3f, test:%.3f, whole:%.3f' % (metrics.r2_score(y_train, y_train_pred), metrics.r2_score(y_test, y_test_pred), metrics.r2_score(y, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (32, 24))\n",
    "plt.subplot(311)\n",
    "plt.plot(range(len(y_train)), y_train_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_train)), y_train, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"训练集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(312)\n",
    "plt.plot(range(len(y_test)), y_test_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"测试集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(313)\n",
    "plt.plot(range(len(y)), y_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y)), y, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"样本集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择各聚类线性模型的特征的交集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = samples_data[list(set(model_features_1st.keys()).intersection(set(model_features_2nd.keys())))]\n",
    "y = samples_data[[('产品性质', 'RON损失\\n（不是变量）')]]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "print('X_train.shape={}\\ny_train.shape={}\\nX_test.shape={}\\ny_test.shape={}'.format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 简单线性回归\n",
    "# Applying LinearRegression\n",
    "model_4th = Lasso(alpha=0.1)\n",
    "model_4th.fit(X_train, y_train)\n",
    "y_train_pred = model_4th.predict(X_train)\n",
    "y_test_pred = model_4th.predict(X_test)\n",
    "y_pred = model_4th.predict(X)\n",
    "print ('MSE train:%.3f, test:%.3f, whole:%.3f' % (metrics.mean_squared_error(y_train, y_train_pred), metrics.mean_squared_error(y_test, y_test_pred), metrics.mean_squared_error(y, y_pred)))\n",
    "print ('R^2 train:%.3f, test:%.3f, whole:%.3f' % (metrics.r2_score(y_train, y_train_pred), metrics.r2_score(y_test, y_test_pred), metrics.r2_score(y, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize = (32, 24))\n",
    "plt.subplot(311)\n",
    "plt.plot(range(len(y_train)), y_train_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_train)), y_train, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"训练集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(312)\n",
    "plt.plot(range(len(y_test)), y_test_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y_test)), y_test, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"测试集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.subplot(313)\n",
    "plt.plot(range(len(y)), y_pred, 'b', label=\"predict\")\n",
    "plt.plot(range(len(y)), y, 'r', label=\"test\")\n",
    "plt.legend(loc = \"upper right\", prop={'size': 14})\n",
    "plt.xlabel(\"样本集\")\n",
    "plt.ylabel(\"辛烷值RON损失\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python(chenhao)",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
